ArticleFrontiers in surgery2023
Establishment of clinical predictive model based on the study of influence factors in patients with colorectal polyps.
Article in Frontiers in surgery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Development of an Interpretable Triage Tool for Colorectal Polyp Risk Stratification Within a Population-Based Screening Program: Machine Learning Approach.JMIR medical informatics · 2026Article
- Construction and validation of machine learning-based predictive model for colorectal polyp recurrence one year after endoscopic mucosal resection.World journal of gastroenterology · 2025Article
- Random forests algorithm using basic medical data for predicting the presence of colonic polyps.Frontiers in surgery · 2025Article
- Development and Validation of Machine Learning Algorithms for Prediction of Colorectal Polyps Based on Electronic Health Records.Biomedicines · 2024Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Colorectal cancer (CRC) is the most common gastrointestinal malignancy and is generally thought to be caused by the transformation of colorectal polyps. It has been shown that early detection and removal of colorectal polyps may reduce the mortality and morbidity of colorectal cancer. Objective: Based on the risk factors associated with colorectal polyps, an individualized clinical prediction model was built to predict and evaluate the possibility of developing colorectal polyp. Methods: A case-control study was conducted. Clinical data were collected from 475 patients who underwent colonoscopy at the Third Hospital of Hebei Medical University from 2020 to 2021. All clinical data were then divided into training sets and validation sets by using R software (7:3). A multivariate logistic analysis was performed to identify the factors associated with colorectal polyps according to the training set, and a predictive nomogram was created by R software based on the multivariate analysis. The results were internally validated by receiver operating characteristic (ROC) curves, calibration curves, and externally validated by validation sets. Results: Multivariate logistic regression analysis showed that age (OR = 1.047, 95% CI = 1.029-1.065), history of cystic polyp (OR = 7.596, 95% CI = 0.976-59.129), and history of colorectal diverticulums (OR = 2.548, 95% CI = 1.209-5.366) were independent risk factors for colorectal polyps. History of constipation (OR = 0.457, 95% CI = 0.268-0.799) and fruit consumption (OR = 0.613, 95% CI 0.350-1.037) were protective factors for colorectal polyps. The nomogram demonstrated good accuracy for predicting colorectal polyps, with both C index and AUC being 0.747 (95% CI = 0.692-0.801). The calibration curves showed good agreement between the predicted risk by the nomogram and real outcomes. Both internal and external validation of the model showed good results. Conclusion: In our study, the nomogram prediction model is reliable and accurate, which can help early clinical screening of patients with high-risk colorectal polyps, improve polyp detection rate, and reduce the incidence of colorectal cancer (CRC).
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.